We consider a variant of the GFP task from [1]}. The vocabulary of amino acids is the same as §: [`A', `C', `D', `E', `F', `G', `H', `I', `K', `L', `M', `N', `P', `Q', `R', `S', `T', `V', `W', `Y']. Following [1]}, we consider the dataset of 56,086 proteins from [3]} processed based on [4]}. Each protein is accompanied by a score quantifying its fluorescence. As with the AMP data, we keep \(20\%\)  of the data as a validation set used for early-stopping. The regressor trained with the dataset is a Transformer, with 4 hidden layers, hidden dimension 64, and 8 attention heads. We train it with a minibatch of size 256, with learning rate \(10^{-4}\) , with early stopping on the validation set. The architecture of the policy for all methods is a Transformer with 3 hidden layers with hidden dimension 64 with 8 attention heads. All methods are trained for \(20,000\)  iterations, with a minibatch size of 16. We use the same implementation for all methods as the ones used in §.
